Using social media to understand drivers of urban park visitation in the Twin Cities, MN

Donahue, Marie L.; Keeler, Bonnie L.; Wood, Spencer A.; Fisher, David M.; Hamstead, Zoé A.; McPhearson, Timon

doi:10.1016/j.landurbplan.2018.02.006

Cited by 209 publications

(123 citation statements)

References 54 publications

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“…This is the first study to our knowledge that develops and tests models for estimating absolute numbers of visitors at unmonitored recreation sites or times using multiple social media data sources with differential effects. Building on earlier research exploring relationships of park visitation with numbers of posts to multiple social media platforms 9,12,20,27 , the present study tests whether models with a mixture of predictors to represent varying effects of three online platforms can estimate visitation in novel situations. We find that each social media data source contributes information that explains a statistically significant portion of the variability in visitation and improves the accuracy of the estimate.…”

Section: Discussionmentioning

confidence: 99%

“…Many recent studies have proposed that volunteered geographic data from social media can complement existing information about visitor distributions, behaviors, and preferences [3][4][5][6][7][8] . Studies spanning an impressive diversity of developed and undeveloped settings have concluded that the popularity of parks is generally mirrored in the popularity of the same destinations on individual social media platforms such as Flickr, Instagram, Sina Weibo, and Twitter [9][10][11][12][13][14][15] (but see 16 ). The consensus emerging from these correlational studies is that data from social media have potential to inform estimates of absolute visitation at specific destinations and for multiple time periods [17][18][19] .…”

Section: Introductionmentioning

confidence: 99%

“…This may be due to differences in who uses various platforms 21,22 , how users share content 23,24 , and changes in the popularity of platforms over time 15,25,26 . Some studies suggest that these issues may be partly overcome by combining data from multiple social media platforms 4,9,12,17,20,27 . However, data quality and interpretability varies across users and service providers 28 , as do restrictions on which, when, and how data can be accessed 29 .…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Next-generation Visitation Models using Social Media to Estimate Recreation on Public Lands

Wood

Winder

Lia

et al. 2020

Preprint

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Outdoor and nature-based recreation provides countless social benefits, yet public land managers often lack information on the spatial and temporal extent of recreation activities. Social media is a promising source of data to fill information gaps because the amount of recreational use is positively correlated with social media activity. However, despite the implication that these correlations could be employed to accurately estimate visitation, there are no known transferable models parameterized for use with multiple social media data sources. This study tackles these issues by examining the relative value of multiple sources of social media in models that estimate visitation at unmonitored sites and times across multiple destinations. Using a novel dataset of over 30,000 social media posts and 286,000 observed visits from two regions in the United States, we compare multiple competing statistical models for estimating visitation. We find social media data substantially improve visitor estimates at unmonitored sites, even when a model is parameterized with data from another region. Visitation estimates are further improved when models are parameterized with on-site counts. These findings indicate that while social media do not fully substitute for on-site data, they are a powerful component of recreation research and visitor management.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Next-generation Visitation Models using Social Media to Estimate Recreation on Public Lands

Wood

Winder

Lia

et al. 2020

Preprint

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“…Points of Interest (POI) are particular location of points, for example, shopping malls, office, restaurant, open spaces, which provide a tool for location-based services. The POI data was chosen because: (1) POI data has good adaptability for scaling problems, (2) by people's interaction, POIs help show their personal biases and also a place's social roles (3) statistical gridding of POI data is much finer, hence providing more comprehensive information [58,59].…”

Section: Data Collectionmentioning

confidence: 99%

Role of Urban Public Space and the Surrounding Environment in Promoting Sustainable Development from the Lens of Social Media

2019

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The development of mobile social network has shown the power to change the ways people gather and communicate in urban public spaces (UPSs). In this study, we utilized a check-in database collected from Instagram in 2016 and 2017 in two central districts of Ho Chi Minh City (HCMC) to analyze the city dynamics and activities over the course of the day. By quantifying the popularity of contemporary UPSs, a comprehensive study was conducted on many attraction features spreading over the two central districts. Pearson’s correlation was used to explore the proximity and attractiveness associated with the surrounding environment of three types of UPSs. The results show that the lifetime of UPSs is very stable during weekdays and weekends. Within that, commercial UPSs are proved to play a dominant role in urban dynamics. This paper’s finding is at odds with the urban planning stereotype that public facilities often help people to get around. In the case of HCMC, it has proved the opposite: people are attracted to urban public spaces even though there are not many cultural and social specialties there. The results will contribute to enhancing the predictability of each UPS on socio-economic performance and to understanding of the role of urban facilities in urban sustainability.

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“…This topic gradually became the subject of urban morphology and urban life research in the middle of the 1970s [6]. In addition, public space was always the most important carrier of public life in Western cities, The quantitative analysis of urban public spaces provide the following: the quantification of the public space form, mainly through morphological research methods, taking the specific city as an example to explore measurement methods of different urban spatial forms [32,33]; the quantification of the social function of public space, constructing the measurement method of the dynamics of people in the space by applying the principle of empirical mathematics [34]; the quantification of the behavioral characteristics of public space users, such as the behavior observation method [4], cognitive map method [14], activity node method [35] and so on, which are commonly used in the field of environmental behavior; the quantification of the green space layout, such as that of green space pattern, accessibility, and access equity based on GIS and other spatial analysis methods [23][24][25]; and exploration based on the big data platform, such as the analysis of the equity distribution of green space [36], park use, and its influencing factors [37]. However, the existing quantitative methods of urban public space cannot effectively quantify the general pattern of public space.…”

Section: Introductionmentioning

confidence: 99%

Revealing Urban Public Space Patterns through Quantitative Comparison between the Old City of Nanjing and Zurich

Wang

2019

Sustainability

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Urban public space is indispensable in a metropolitan environment. In recent years, green space, as an important part of that public space, has been studied in terms of its pattern and equity of accessibility. However, the pattern of urban public space, including streets, has not yet been studied, nor has it been studied among different countries. To resolve this gap in information, this study conducted a quantitative comparison on the general pattern, type pattern, and scale pattern of urban public space between Zurich in Switzerland and the old city of Nanjing in China. This study also explored using the location quotient method to quantify the pattern of urban public space and its physical structure characteristics. The results show the difference between urban public space in China and Europe exists not only in quantity, scale or type, but also in the pattern of the urban public space system, including the choice of location, distribution status, and service level of that public space. The maximum location quotient of Zurich’s public space is 9.5 for the areas located in the urban core area. Meanwhile, it is 8.5 for Nanjing for areas located in the periphery of the old city. Areas with a location quotient of greater than one cover 63.3% of the urban construction land in Zurich, while only 30.8% of the corresponding urban construction land is covered in Nanjing. The area and quantity of streets are quite different as well. The street areas of Zurich account for 51.5% of the total area of public space and the number of streets account for 51.2% of the total number of public space sites, while those numbers are only 22.6% and 17.5% for Nanjing, respectively. In addition, the scale gradient of public space is polarized. Both the area ratio and quantity ratio of medium, medium-large, and large public space in Zurich exceed 92%, while the area ratio and quantity ratio of Nanjing are less than 77% and 68%, respectively. This study provides important insights for revealing urban public space patterns to facilitate the sustainability development of urban public space.

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Using social media to understand drivers of urban park visitation in the Twin Cities, MN

Cited by 209 publications

References 54 publications

Next-generation Visitation Models using Social Media to Estimate Recreation on Public Lands

Next-generation Visitation Models using Social Media to Estimate Recreation on Public Lands

Role of Urban Public Space and the Surrounding Environment in Promoting Sustainable Development from the Lens of Social Media

Revealing Urban Public Space Patterns through Quantitative Comparison between the Old City of Nanjing and Zurich

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